My research program emphasizes in the exploration, development and implementation of control and estimation methods to address real-world problems via provably-correct solutions. I am particularly interested in multi-agent systems operating in uncertain environments (e.g., unstructured and/or adversarial).
Some of the ongoing and past research programs are described below.
A Human-centric Network of Free-Flying Co-Robots: We envision to ultimately augment astronauts in spacewalk with the Astronet: A Network of Astronautical Free-Flying Co-Robots, such as SPHERES or Astrobees developed by NASA. The Astronet will safely surround the crew member during EVAs, infer human intent and interpret them into predefined tasks, and respond to human inputs by redistributing autonomously in space to dynamically and continually improve task conditions in a human-centric way. We link our dynamic coverage control paradigm for the swarm motion with inference methods to online learn the task-relevant cues of the human while executing multiple tasks. We also motion models and sensing uncertainty for the 3D flight of free floating robots, and energy consumption models capturing their limited power resources.
This research is sponsored by the NASA Space Technology Research Grants Program through an Early Career Faculty Award "https://www.nasa.gov/directorates/spacetech/strg/ecf2016/AstroNet.html"
W. Bentz, S. Dhanjal and D. Panagou "Unsupervised Learning of Assistive Camera Views by an Aerial Co-robot in Augmented Reality Multitasking Environments", Int. Conf. on Robotics and Automation, Montreal, Canada, May 2019.
W. Bentz and D. Panagou "Bayesian-inferred Flexible Path Generation in Human-Robot Collaborative Networks", Int. Conf. on Robots and Intelligent Systems, Madrid, Spain, October 2018.
On-the-Fly Assistive-View Learning in Augmented Reality Multitasking Environments
Human-Robot (Astrobee) Interaction in a Virtual Reality Environment of the International Space Station
Human-Robot Interaction for Dynamic Coverage via Gesture Following
Bayesian-inferred Human Intention and Flexible Robot Trajectory Generation
The goal of this research project is to narrow the existing gap between high-level discrete task planning and low-level continuous control in complex multi-agent missions. We consider high-level spatiotemporal specifications (e.g., state and time constraints on the trajectories of a multi-agent system) encoded via signal temporal logic, and develop a framework to synthesize the control inputs that realize the given specifications. We have introduced the concept of Fixed-Time Control Lyapunov Functions to enable convergence of the system trajectories to the origin within a given, fixed time. Along with Control Barrier Functions (CBFs), FxT-CLFs encode a class of spatiotemporal specifications that can be enforced via control inputs computed via quadratic programming. The feasibility of the underlying QPs is also studied; it is shown that slack variables can explicitly prescribe regions of initial conditions from which both safety and time constraints can be met. In parallel, we have extended classical results on the stability of switched and hybrid systems to multiple Lyapunov Functions that analyze stability or achieve stabilization of system trajectories in finite time.
This research is sponsored by the Air Force Office of Scientific Research through an Young Investigator Award "http://www.wpafb.af.mil/News/Article-Display/Article/969772/afosr-awards-grants-to-58-scientists-and-engineers-through-its-young-investigat/"
K. Garg, E. Arabi and D. Panagou, "Fixed-time control under spatiotemporal and input constraints: A QP-based approach", revised and resubmitted, under review
K. Garg and D. Panagou "Control-Lyapunov and Control-Barrier Functions based Quadratic Program for Spatio-temporal Specifications", 58th IEEE Conference on Decision on Control, Nice, France, December 2019
K. Garg and D. Panagou "New Results on Finite-Time Stability: Geometric Conditions and Finite-Time Controllers", 2018 American Control Conference, Milwaukee, Wisconsin, June 2018
Path planning under spatiotemporal specifications
We consider resilience AND safety of multi-agent networks (e.g., multi-vehicle systems) in adversarial environments. Safety is viewed as the guaranteed collision-free motion of the agents while collaborating towards a common task (e.g., data gathering). Collaboration in principle requires coordination and negotiation mechanisms among the agents; these mechanisms are implemented using information shared over wireless communication links. However, wireless communication is vulnerable to cyber-attacks.
Resilience is hence viewed as the guaranteed accomplishment of the mission, despite the presence of possible adversaries that can send malicious data over compromised communication links. Our goal is to establish resilient communication structures, as well as estimation (filtering) and control mechanisms that will allow the multi-agent network to tolerate or mitigate the adversarial effects of malicious data in the network, while still maintaining safety guarantees.
Our premilinary work includes the establishment of k-circulant graphs as a sufficient communication topology for achieving resilient asymptotic consensus, as well as the extension of strong r-robustness graphs to achieve consensus to arbitrary reference values, that can be used in leader-follower networks. We have also developed methods for resiliently propagating information across leader-follower networks despite the effect of adversarial agents (that can be both leaders and followers). We are currently working on incorporating Control Barrier Functions for high-relative degree systems to guarantee safety despite the effects of adversarial agents in the network.
This research is sponsored by Automotive Research Center and US Army TARDEC under the project "Advesarially Robust Coordination for Autonomous Multi-Vehicle Systems", and by the Army Research Office (ARO) under Award No W911NF-17-1-0526.
Multi-Source Resilient Propagation Algorithm (MSRPA)
J. Usevitch and D. Panagou, "Resilient Trajectory Propagation in Multi-Robot Networks", submitted, under review
J. Usevitch and D. Panagou "Resilient Finite-Time Consensus: A Discontinuous Systems Perspective", 2020 American Control Conference, Denver, CO, July 2020
J. Usevitch and D. Panagou "Resilient Leader-Follower Consensus with Time-Varying Leaders in Discrete-Time Systems", 58th IEEE Conference on Decision on Control, Nice, France, December 2019
J. Usevitch, K. Garg and D. Panagou "Finite-Time Resilient Formation Control with Bounded Inputs", 57th IEEE Conference on Decision on Control, Miami, FL, December 2018
J. Usevitch and D. Panagou "Resilient Leader-Follower Consensus to Arbitrary Reference Values", 2018 American Control Conference, Milwaukee, Wisconsin, June 2018.
J. Usevitch and D. Panagou "r-Robustness and (r,s)-Robustness of Circulant Graphs", 56th IEEE Conf. on Decision and Control, Melbourne, Australia, December 2017.
We are studying and developing defense strategies against aerial swarms of small UAS. In our earlier work we studied secure (resilient) communication topologies that achieve resilient formation control for aerial swarms in the presence of malicious information from attackers. We then developed control laws for a swarm of defender agents that herd an attacking swarm to a safe area in obstacle-populated environments. More recently, we consider more complicated behaviors by the attackers, such as splitting into smaller swarms. Apart from herding solutions, we also have studied strategies for the defenders to capture/intercept the attackers. Our goal end is to combine herding and capturing solutions to provide a complete (under certain conditions) defending strategy against attackers.
This work has been funded by the Center for Unmanned Aircraft Systems (C-UAS), a National Science Foundation Industry/University Cooperative Research Center (I/UCRC) under NSF Award No. 1738714 along with significant contributions from CUAS industry members.
V. S. Chipade and D. Panagou "Multi-Agent Planning and Control for Swarm Herding in 2D Obstacle Environments under Bounded Inputs", under review
V. S. Chipade and D. Panagou "Multi-Swarm Herding: Protecting against Adversarial Swarms", 59th IEEE Conference on Decision on Control, Jeju Island, Republic of Korea, December 2020
V. Chipade and D. Panagou "Herding an Adversarial Swarm in an Obstacle Environment", 58th IEEE Conference on Decision on Control, Nice, France, December 2019
V. Chipade and D. Panagou "Herding an Adversarial Attacker to a Safe Area for Defending Safety-Critical Infrastructure", 2019 American Control Conference, Philadelphia, PA, July 2019
V. Chipade and D. Panagou "Multiplayer Target-Attacker-Defender Differential Game: Pairing Allocations and Control Strategies for Guaranteed Intercept", 2019 AIAA Science and Technology Forum and Exposition (Scitech) Forum, San Diego, CA, January 2019
Swarm Herding (Gazebo)
Dynamic coverage is defined as forcing an agent to sense/cover over time each point of a domain of interest up to a satisfactory level. For agents with sensing functionals defined over finite footprints, this formulation results in algorithms which set them in motion based on how well they sense the surrounding environment. Thus agents are forced to autonomously and continually explore an unknown region (search) so that each point of this region is sensed for a prescribed amount of time. This requirement is encoded in a coverage metric that expresses the quality of information accumulated over time through the agent’s sensing footprint. The dynamic coverage problem then reduces to deriving the control laws for the motion of the agents so that the associated coverage error is driven to zero. These control laws force the agents to autonomously move towards, and consequently explore, non-searched regions.
We have developed energy-aware decentralized control algorithms for the motion of agents performing coverage tasks, along with collision avoidance guarantees under anisotropic sensing that creates asymmetric interactions among agents.
D. Panagou, D. M. Stipanovic and P. G. Voulgaris "Distributed dynamic coverage and avoidance control under anisotropic sensing", IEEE Transactions on Control of Network Systems, vol. 4, no. 4, pp. 850-862, 2017.
Aerial Sensing Networks in 3D Environments: We have extended our dynamic coverage control approach to the problem of increased and sustained 360 Situational Awareness in 3D environments. We developed an Aerial Sensing Network of small UAVs, which are actively exploring the area around a Ground Station by means of Energy-Aware Dynamic Coverage. Planning and control are tailored to the 3D motion constraints of small UAVs, the limitations of data links, cameras and other onboard sensors, as well as on the UAVs' energy (battery life) constraints. 3D coverage metrics capturing visual information gathering and navigation in 3D spaces yield control algorithms that ensure effective 3D visual coverage. The remaining battery life of each agent is taken into account so that power-constrained agents are redistributed in space; this allows those agents with longer battery life to explore farther away from the Ground Station, and those with shorter battery life to return safely to the Ground Station. Collision-free trajectories are generated in real-time through novel 3D coordination and collision avoidance algorithms with provable guarantees.
This research was sponsored by the Automotive Research Center and US Army TARDEC under the project "SQUAD: Situational Awareness and Sustained Survivability through Man/Unmanned Teaming" "http://arc.engin.umich.edu/research/5_A46_Situational_Awareness.html"
W. Bentz and D. Panagou "A Hybrid Approach to Persistent Coverage in Stochastic Environments", Automatica, vol. 109, 108554, pp. 1-12, November 2019
W. Bentz, T. Hoang, E. Bayasgalan and D. Panagou "Complete 3-D Dynamic Coverage in Energy-constrained Multi-UAV Sensor Networks", Autonomous Robots, vol. 42, no. 4, pp. 825-851, April 2018
Outdoors Flight over the University of Michigan Wave Field
We develop distributed coordination algorithms for multi-agent systems while respecting certain safety and performance guarantees. Safety is realized as the collision-free navigation of the agents towards goal locations under restricted sensing and communication capabilities. Performance is primarily realized as the robustness of the derived solutions against communication/sensing uncertainty and malicious behavior by the agents.
We address multi-task problems (such as collision avoidance, connectivity maintenance and convergence to goal destinations) for networks of mobile agents via a novel class of Parametric Lyapunov-like Barrier Functions. The Parametric Lyapunov-Barrier Functions capture safety and convergence specifications, as well as sensing and communication misinformation among the agents, and ensure robust connectivity and safety for the agents against the considered disturbances. Our next goal is to extend the methodology to realistic models for a wide class of autonomous vehicles.
We also consider coordination for agents belonging to different classes, namely class-A and class-B. Agents of class-B do not share information with agents of class-A and do not participate in ensuring safety, modeling thus agents with failed sensing/communication systems, agents of higher priority, or moving obstacles with known upper bounded velocity. We propose the notion of semi-cooperative coordination: Semi-cooperative coordination is defined as the ad-hoc prioritization among agents of the same class; more specifically, participation in conflict resolution and collision avoidance for each agent is determined on-the-fly based on whether the agent's motion results in decreasing its distance with respect to its neighbor agents; based on this condition, the agent decides to either ignore its neighbors, or adjust its velocity and avoid the neighbor agent with respect to which the rate of decrease of the pairwise inter-agent distance is maximal. Guarantees on safety and almost global convergence of the agents to their destinations are formally proved.
Agents of Class-A and Class-B (in V-shape formation).
D. Han and D. Panagou "Robust Multi-task Formation Control via Parametric Lyapunov-like Barrier Functions", IEEE Transactions on Automatic Control, vol. 64, no. 11, pp. 4439-4453, November 2019
D. Panagou "A Distributed Feedback Motion Planning Protocol for Multiple Unicycle Agents of Different Classes", IEEE Transactions on Automatic Control, vol. 62, no. 3, pp. 1178-1193, March 2017.
D. Panagou, D. M. Stipanovic and P. G. Voulgaris "Distributed coordination control for multi-robot networks using Lyapunov-like barrier functions", IEEE Transactions on Automatic Control, vol. 61, no. 3, pp. 617-632, March 2016.
Deconfliction in Multi-sUAS Missions: We considered multi-sUAS missions with heterogeneous aircraft (fixed-wing and multi-copters). Our focus was on designing computationally efficient mechanisms that deconflict multiple agents in the presence of sensing uncertainty, wind, and physical sobstacles. The proposed work will enable fast and accurate trajectory generation for self-separation in complex environments.
This research was supported by the NASA Grant NNX16AH81A.
K. Garg and D. Panagou "Finite-Time Estimation and Control for Multi-Aircraft Systems under Wind and Dynamic Obstacles", AIAA Journal of Guidance, Control, and Dynamics, vol. 42, no. 7, pp. 1489-1505, July 2019
K. Garg and D. Panagou "Hybrid Planning and Control for Multiple Fixed-Wing Aircraft under Input Constraints", Best Student Paper Finalist, 2019 AIAA Science and Technology Forum and Exposition (Scitech) Forum, San Diego, CA, January 2019
K. Garg, D. Han and D. Panagou "Robust Semi-Cooperative Multi-Agent Coordination in the Presence of Stochastic Disturbances", 56th IEEE Conf. on Decision and Control, Melbourne, Australia, December 2017.
X. Ma, Z. Jiao, Z. Wang and D. Panagou "3D Decentralized Prioritized Motion Planning and Coordination for High-Density Operations of Micro Aerial Vehicles", IEEE Transactions on Control Systems Technology, May 2017, DOI: 10.1109/TCST.2017.2699165.
Deconfliction for Double-Integrator Agents.
Aircraft (Fixed-Wing) Deconfliction via Hybrid Control.
Decentralized Goal Assignment and Trajectory Generation via Multiple Lyapunov Functions | Collaborative work with UPenn: We develop decentralized feedback control policies and coordination protocols for multi-robot systems with certain safety and performance guarantees. Safety is realized as the collision-free motion towards goal locations under restricted sensing and communication capabilities, while performance is realized as the assignment of goals which result in shortest total distance to the goal locations. The formulation within a Multiple Lyapunov-like Barrier Functions approach enables scalable and correct-by-construction algorithms, which perform well for hundreds of agents.
D. Panagou, M. Turpin and V. Kumar "Decentralized goal assignment and trajectory generation in multi-robot networks: A multiple Lyapunov functions approach", 2014 IEEE Int. Conf. on Robotics and Automation, Hong Kong, China, June 2014
Visibility Maintenance for Leader-Follower Formations in Obstacle Environments: We consider GPS-denied obstacle environments where multiple robots need to coordinate their motion using vision-based sensing systems only, in the absence of explicit information exchange. Physical obstacles may obstruct visibility, therefore effective sensing, and furthermore should always be avoided.
We develop decentralized motion coordination algorithms for formations of mobile robots in such constrained environments, which guarantee the collision-free motion of the robotic network and the maintenance of visibility among robotic agents.
D. Panagou and V. Kumar "Cooperative visibility maintenance for leader-follower formations in obstacle environments", IEEE Transactions on Robotics, vol. 30, no. 4, pp. 831-844, Aug. 2014
Dynamic Positioning and Formation Control for Underactuated Marine Vehicles: Guidance, navigation and control of marine vehicles (ships, surface vessels and underwater vehicles) is an active research topic, motivated in part by the extensive use of autonomous vehicles in oil industry, scientific explorations (e.g. in oceanographic, archaeological and marine biology research), search and rescue missions, surveillance and inspection tasks, etc. The underwater environment, in particular, poses additional challenges to guidance, navigation and control tasks due to the lack of GPS measurements. Thus, vision is often the main means of sensing and localization with respect to targets of interest.
We develop hybrid and switching control algorithms for underactuated vehicles which move in the presence of unknown external disturbances and vision-based constraints, which guarantee the practical stability of the system with respect to a target of interest. Trade-offs between visibility maintenance and accurate positioning are studied and analyzed. We also consider multi-vehicle formation control so that multiple marine vehicles maintain visibility with, and eventually encircle, a target of interest.
Our next goals include the extension of the methodologies to docking and collaborative control problems for satellites and spacecraft.
D. Panagou and K. J. Kyriakopoulos "Viability control for a class of underactuated systems", Automatica, 49 (2013), pp. 17-29
D. Panagou and K. J. Kyriakopoulos "Dynamic positioning for an underactuated marine vehicle using hybrid control", International Journal of Control, 2013, http://dx.doi.org/10.1080/00207179.2013.828853
D. Panagou and K. J. Kyriakopoulos "Cooperative formation control of underactuated marine vehicles for target surveillance under sensing and communication constraints", 2013 IEEE Int. Conf. on Robotics and Automation, Karlsruhe, Germany, May 2013